In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [4]:
import numpy as np

fig, ax = plt.subplots(figsize=(12,9))

x = stocks['date']
y = stocks['GOOG']
plt.plot(x,y)

ax.set_xticks(np.arange(0, len(x)+1, 14)) #Length of file = 105, steps of 14 makes 8 dates show in the x-axis.

ax.set_xlabel('date')
ax.set_ylabel('stock value')
ax.set_title('Google stock');

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [5]:
stocks.plot(x = 'date', figsize=(12,9), xticks=np.arange(0, len(x)+1, 14), xlabel = 'date', ylabel = 'stock value', title = 'Stocks');

Seaborn

First, load the tips dataset

In [6]:
tips = sns.load_dataset('tips')
tips.head()
Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [7]:
print("Question: Are there differences between male and female when it comes to giving tips?")

sns.scatterplot(x='total_bill', y='tip', data=tips, hue = 'sex')
plt.show()  

print("Answer: The scatterplot shows that give a little more tip than women.") 
print("The average tip men give is", 
      round(tips[tips.sex == 'Male'].tip.mean(),2), ", while women on average give a tip of", 
      round(tips[tips.sex == 'Female'].tip.mean(),2),'.');

Question: Are there differences between male and female when it comes to giving tips?

Answer: The scatterplot shows that give a little more tip than women.
The average tip men give is 3.09 , while women on average give a tip of 2.83 .

Plotly Express

Question 4:

Redo the above exercises (question 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [8]:
stock_names = stocks.columns[1:]
print(stock_names)

fig1 = px.line(stocks, x = 'date', y = stock_names, markers = True)
fig1.show();

Index(['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'], dtype='object')

The tips dataset

In [9]:
#fig2 = px.scatter(tips, x='total_bill', y='tip', color='sex')
#fig2.show()

fig1 = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="smoker", facet_row="time")
fig1.show(figsize = (12,9));

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [10]:
#load data
df = px.data.gapminder()
df.head()
Out[10]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [11]:
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()

fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, orientation='h', 
             color = df_2007_new["pop"].astype(str), text='pop')

fig.update_yaxes(categoryorder="total ascending")

fig.update_traces(textposition='outside', showlegend=False,texttemplate = "%{text:.2s}")
fig.update_layout(uniformtext_minsize=12)

fig.show()